A Multitheoretical, Multilevel, Multidimensional … Network Model...Running head: A NETWORK MODEL OF THE MEDIA SYSTEM A Multitheoretical, Multilevel, Multidimensional Network Model

Running head: A NETWORK MODEL OF THE MEDIA SYSTEM

A Multitheoretical, Multilevel, Multidimensional Network Model of the

Media System: Production, Content, and Audiences

Katherine Ognyanova and Peter Monge

University of Southern California

Citation: Ognyanova, K., & Monge, P. (2013). A Multitheoretical, Multilevel, Multidimensional Network Model of the Media System: Production, Content, and Audiences. Communication yearbook, 37, 66-93.

Authors Note

The preparation of this article was supported by a grant from the National Science Foundation

(IIS-0838548) and by funding to the Annenberg Networks Network from the Annenberg School

for Communication and Journalism. The authors would like to express their appreciation to

Amanda Beacom, Janet Fulk, Nina O'Brien, Peter Knaack, and the Communication Yearbook

reviewers for their helpful comments on earlier drafts of the paper.

A NETWORK MODEL OF THE MEDIA SYSTEM 2

Abstract

This article examines the network mechanisms which underlie major parts of the media system:

the industry, the content, and the audience. It identifies key theoretical frameworks that can be

used to explain the formation and dissolution of ties in each of those areas. The paper presents a

relational reinterpretation of classic mass communication theories and advocates the use of a

multitheoretical, multilevel, and multidimensional network approach to media studies research.

The article discusses the network structures behind media production, output and consumption.

We examine interorganizational ties (industry sector), semantic relations (content sector), and

social bonds (audience sector). Key studies and theoretical mechanisms that bridge two or all

three of those domains are reviewed.

Keywords: media studies, media effects, network analysis, network theory, agenda-

setting, framing, diffusion, hyperlink analysis, semantic network


A Multitheoretical, Multilevel, Multidimensional Network Model of the

Media System: Production, Content, and Audiences

The systematic study of social relationships and structures has a long tradition across

disciplines including communication, media studies, sociology, social psychology, and

anthropology (Freeman, 2004). Since the 1970s the volume of published network research in

particular has grown exponentially, marking a gradual shift from individualist to relational

scientific explanations (Borgatti & Foster, 2003). In the last decade, technological advances have

contributed to a boom in academic studies of diverse networks (Watts, 2004). Information and

communication technologies have enhanced networked forms of organization, bringing to light

previously implicit social relations (Castells, 2005). Technological innovations have also

provided scientists with the data collection tools and computational capacity needed to develop

resource-intensive network-analytical techniques.

The rapid progress of network theories and methods prompted a wave of studies offering

systematic overviews of network processes and their integration in existing theoretical

frameworks from a variety of academic fields (Carrington, Scott, & Wasserman, 2005). Scholars

have investigated the implications of a relational approach in the areas of organizational

communication (Monge & Contractor, 2003), health (Valente, 2010), economics (Schweitzer et

al., 2009), management (Borgatti & Foster, 2003), warfare (Arquilla & Ronfeldt, 2001), and

Web science (Turow & Tsui, 2008), among others.

Building on that foundation, this article examines the mechanisms and frameworks

explicating communication flows and relational ties in the context of media studies. Our

premises are consistent with theories of mass communication examining media effects in the

context of larger social structures (Ball-Rokeach, 1985). The framework explores the dynamic

relationships between audience members and news organizations. Those ties of influence are

seen as grounded in the capacity of media sources to produce content serving individual

information and social goals (Ball-Rokeach, Rokeach, & Grube, 1984). In view of that, we adopt

a broad definition of the media system that comprises media organizations, the content they

produce, and its consumers. We identify the relevant nodes and links underlying those three


major components (see Figure 1). The media industry sector (or domain1), is characterized by

links among news organizations. Media content is grounded in ties between key terms, concepts,

frames and news stories. The third part of the media system, audiences, consists of connected

news consumers. Although in principle the range of available media is very broad and may

encompass film, music, games and more, for the sake of simplicity and clarity this work focuses

specifically on news media.

The analysis begins with a discussion of the networks within each specific media domain,

called intra-sector networks. These networks are comprised of nodes and links belonging to a

single sector, such as media organizations linked by ownership patterns (industry sector, see

Figure 1, [1-1]) or networks of news stories linked through semantic similarities (content sector,

see Figure 1, [2-2]). Intra-sector structures can also be multiplex. That is to say, they may include

more than one type of link within a single domain. One example of this is an industry network

with both ownership and partnership ties (Arsenault & Castells, 2008a).

The article then explores the interactions that occur between sectors. We review inter-

sector networks - structures that involve nodes from a focal domain, and additional nodes and/or

relations from one or both of the other two domains. For instance media organizations could be

connected through content-based ties, such as the hyperlinks between their websites (see Figure

1, [1-2]). Affiliation networks (see Figure 1, [1-3] [3-1]) which link individual news consumers

(audience nodes) to their preferred news organizations (industry nodes) are also considered inter-

sector. We further discuss the more complex patterns that emerge from interdependencies

between different parts of the media system. Such structures may contain multiple different kinds

of nodes (e.g. individuals and news organizations) and multiple kinds of relations (e.g. social and

mediated). The former type of network is known as multimodal and the latter type is called

multiplex (or multirelational). Inter-sector networks may also be multidimensional (Contractor,

Monge, Leonardi, 2011). This term applies to networks that consist of both multiple kinds of

nodes (multimodal) and multiple kinds of relations (multiplex).

This approach to the study of media facilitates an important shift in theoretical focus. The

emphasis moves from the attributes of organizations, news stories, and consumers to the

1 Throughout this text we use the terms sector and domain interchangeably to refer to the three components of the media system: industry, content, and audience.


similarities, social relations, interactions, and flows of information and resources among them

(Borgatti, Mehra, Brass, & Labianca, 2009). Research can thus investigate the structural

determinants of social, economic and political processes, the network patterns of power and

influence (Castells, 2009), as well as the impact of interpersonal ties on individual preferences

and public opinion (Wasserman & Faust, 1994). This reframing of how media systems operate

does not replace the examination of individual characteristics, such as political attitudes, gender,

and socioeconomic status, nor does it dismiss the explanatory power of content attributes, such

as news, entertainment, or opinion. Rather, it supplements and contextualizes these features by

providing the web of friends, families, coworkers, neighbors, and other sources that supply the

information, norms, values and motivations determining how people select and use various

media.

As Castells (2009) points out, relational thinking becomes even more important in the

context of a media industry moving to networked forms of content production, delivery, and

consumption. We are currently seeing industry-wide trends towards consolidation,

interorganizational collaborations, local and global partnerships (Arsenault & Castells, 2008b).

Online formats and new technologies connect newsrooms and audience members (Cardoso,

2006), making content diffusion faster and easier to track through digital traces (Anderson,

2010). Professional and personal social ties affect individual news consumption and distribution

habits (Boczkowski, 2010). News stories themselves are placed within networks of semantic

relations (Diesner & Carley, 2005) and hyperlink connections (Turow & Tsui, 2008).

An early criticism of network research claimed that the field is overly descriptive: a

combination of algorithms lacking a native theoretical framework (Kilduff & Tsai, 2003). More

recently, scholars working in the area have argued that this claim is no longer true, suggesting

that a rich network theory is now yielding new explanations in a variety of disciplines (Borgatti

et al., 2009). Relational interpretations have been used to redefine key concepts, test existing

theories, and develop new understanding about the structural causes and consequences of social

phenomena (Marin & Wellman, 2010).

In its approach to theory, the present work draws on the multitheoretical, multilevel

(MTML) analytical strategy put forward by Monge and Contractor (2003). Their proposed model

incorporates a range of properties, from individual and dyadic, through more complex structural


patterns, to network-level measures. Studying changes over time as well as the analysis of

separate co-evolving networks are also part of the MTML approach. Monge and Contractor

further argue that explaining these complex multilevel mechanisms requires the use of multiple

theoretical frameworks, some independent, others competing or complementary.

Accordingly, this article identifies a number of theoretical mechanisms explicating the

formation, dissolution and consequence of links in the media system. The theories described in

the following sections come from media studies, organizational communication, economics, web

science, sociology and linguistics. Some of them (e.g., diffusion theories, communication

infrastructure theory) are already framed in network terms. Others, like agenda-setting and

framing, need to be adapted to a relational interpretation.

The model we propose here provides a framework organizing existing knowledge at the

intersection of media studies and network science. Serving as a map of the field, it is intended to

help readers think through the multiple theoretical domains, the various levels of analysis, and

diverse types of linkages involved in a relational perspective on the media system. Similarly to

the more comprehensive work of Monge and Contractor (2003) in the area of organizations, this

study identifies layered explanations of network dynamics in mass communication. It makes

explicit the structures that have remained hidden in previous works exploring the increasingly

networked news media of today. In that effort, the present study aims to synthesize the core

dynamics underlying a number of important works like those of Castells (2009) and McChesney

(2004). As the field of mass communication undergoes a paradigm shift, adopting a relational

perspective will enable scholars to integrate disparate theories, bridge separate levels, and

explore multidimensional linkages, thus filling the gaps in the existing literature.

The value of multilevel network interpretations is also evident in the context of media

theories explicitly employing relational explanations. The two-step flow of communication (Katz

& Lazarsfeld, 1955; Lazarsfeld, Berelson, & Gaudet, 1944) is one classic example, as it brings

together media effects and social structures. Another case in point at the macro level of analysis

comes from the media system dependency theory (Ball-Rokeach, 1985) in its examination of

power relations within and across social systems.

Monge & Contractor’s (2003) work outlining their MTML approach identifies seven

families of social theories that can be applied to communication networks. It explores the


implications of using those theories at multiple levels, offering insights that no single framework

by itself could provide. Adopting this strategy, we examine the multiplicity of theories across

levels of analysis. Important to note, it is not our intention to claim that all of those theoretical

frameworks need to be fused together. We do not suggest that a single empirical analysis should

incorporate and test the full range of mechanisms presented here. Instead, throughout this article

we identify various combinations of theoretical perspectives, analytical tools, and network

definitions that could, when applied together, advance our understanding of the media system.

The judicious selection and integration of theories at multiple levels and alternative types of

linkages should prevent a misuse of the MTML model sometimes referred to as an eclectic

approach to theorizing.

The following sections of the paper provide a systematic overview of relevant network

mechanisms and theoretical frameworks. A summary of the proposed theories is available in

Table 1. Examples providing fruitful directions for future research are presented in the

conclusion of this article.

Intra-Sector Networks

The Industry Sector: Interorganizational Networks [1-1]

From a macro-level perspective, the media industry can be seen as an interconnected set

of corporate actors (see Figure 1, [1-1]), linked through economic, social, political, and cultural

ties (Arsenault & Castells, 2008a). Networked forms of organization, production, and

distribution, which are becoming ubiquitous across industries, are particularly prominent in the

media sector (Castells, 2009). The well-studied ongoing processes of media ownership

concentration (McChesney, 2000, 2004) contribute to the complex layer of dense ownership,

partnership, and cross-investment relationships between news outlets. Deregulation and

digitization have further lead to convergence in the information sector as media,

telecommunication, and technological companies merge and expand (Chon, Choi, Barnett,

Danowski, & Joo, 2003; Danowski & Choi, 1999).


Interorganizational networks have been a subject of inquiry in the fields of sociology and

organizational theory (Baum, 2002). In this type of analysis, individual organizations constitute

the network nodes, each of which may have a set of properties based on the research questions

posed by the study. A wide range of formal and informal relationships can be used to define the

network ties. Baker and Faulkner (2002) list some of the most relevant link types: market

exchanges, strategic alliances, joint participation in syndicates, joint political action, interlocking

directorates, family ties, and illegal activities such as collusion. All of those are applicable to the

media industry which is, in addition, characterized by frequent content/information exchange

relationships.

Another set of ties between companies in the media sector can be constructed on the basis

of human resources (Gulati, Dialdin, & Wang, 2002). This is especially relevant for media

organizations both because the industry is known to have high turnover rates and because

journalists, more so than other professionals, tend to work for multiple organizations.

Two frameworks that capture the relationships outlined above are resource dependency

and social exchange theory (Blau, 1964; Emerson, 1962). These theories seek to explain

interactions and relationships based on the supply, demand, and exchange of material and

information resources (Monge & Contractor, 2003). Initially limited to interactions between two

actors, the frameworks have expanded to explore larger structures going beyond the dyadic

relationship. Participating in a network allows members to gain access to various resources:

financial, institutional, and information-related among others (Gulati et al., 2002). Financial

resources are linked to capital acquisition and investments, while institutional resources have to

do with gaining legitimacy, credibility, and status. Both money and reputation can be

accumulated based on network ties. Knowledge and information resources are particularly

important in the media industry, where information dissemination is crucial and the adoption of

new technologies and practices needs to happen faster than it does in many other sectors (Pew

Project For Excellence in Journalism, 2010).

While network members can benefit from increased financial opportunities, improved

survival chances and enhanced learning capabilities, it is important to note that some patterns of

network ties can also be detrimental (Gulati et al., 2002). A disadvantageous network position

may make companies vulnerable to exclusion from valuable resources (Monge & Contractor,


2003). Furthermore, having extensive ties may prove to be a liability: over-embeddedness can

prevent members from discovering new opportunities existing outside the scope of their network

(Uzzi, 1997).

Monge, Heiss and Margolin (2008) demonstrate that evolutionary and ecological theories

provide a useful lens through which the dynamics of network change can be studied.

Evolutionary theory, by definition a general theory of change, looks into organizational birth,

development, transformation, decline, and death (Baum, 2002). The ecological approach focuses

on the composition of organizational populations and the resource environments in which they

are located (Aldrich & Ruef, 2006). It emphasizes the interdependencies between organizations,

looking into both competitive and cooperative relations. As Monge et al (2008) have pointed out,

the combination of evolutionary and network theory can be applied to the area of mass media,

providing the tools to study the changing interactions between news outlets as they compete for

the scarce resource of the attention of the public. One useful analytical perspective to the

investigation of dynamic interorganizational networks involves the evolutionary mechanism of

V-S-R: variation, selection and retention (Campbell, 1969). Variation refers to the idea of

exploring a range of possibilities, while selection involves accepting some of those options and

rejecting others. Retention describes the persistent maintaining of a selected variation over time.

The three processes can operate on a number of levels - within individual organizations,

populations, and communities. In the context of media industry networks, important sources of

variation exist in partnership and collaboration ties, some of which are selected and retained over

time (Monge et al., 2008). The ecological framework is particularly helpful in the context of one

rather difficult task that empirical research on interorganizational relations presents: defining the

network boundaries. The set of media outlets that need to be included in a research study is not

always immediately obvious. Considering only companies which operate within a certain

geographic region (such as the area where a newspaper is circulated) becomes somewhat

problematic as the Web lifts territorial restrictions on content distribution. In a book taking an

ecological perspective to the media sector, John Dimmick (2003) proposes using the theory of

the niche to define the scope of populations and industries. Organizations are in the same

population - or occupy the same niche - if they compete for the same set of resources. Among the

resource dimensions suggested by Dimmick are consumer time and spending, advertising


revenues, type of gratifications provided by the media (as described in Ball-Rokeach & DeFleur,

1976), and content.

The Content Sector: Semantic Networks [2-2]

Another sector in which networks play a fundamental role is that of content (see Figure 1,

[2-2]). News articles, either individually or as a larger corpus of multiple texts, can be presented

as maps of interrelated concepts. This allows for an exploration of the way issues and ideas are

linked together in journalistic materials. It also facilitates the comparative analysis of different

discourses that develop around contested social issues.

Two major frameworks come together in studies that employ concept maps to analyze

news content. The theoretical background is provided by the media effects theory of framing

(Gamson & Modigliani, 1989; Goffman, 1974). The analytical approach is that of semantic

network analysis (SNA).

Semantic analysis is based on the premise that knowledge can be presented as networks

of words and their relationships to each other in a given context (Carley, 1993). SNA software

identifies the important concepts in a written work based on the frequency of their occurrence.

Some types of words, such as transitive verbs, prepositions, conjunctions, etc., tend to appear

often in any text but are not necessarily considered very important. Those are typically excluded

from the analysis (Murphy & Maynard, 2000). A semantic network, sometimes also called a

semantic map, is constructed based on a set of terms which have been identified as most relevant.

A number of methods have been developed to evaluate the strength of semantic

relationships. The ties in a semantic map are typically based on related or overlapping meaning.

One simple and often used way to detect a link between two words is based on the frequency of

their proximate co-occurrence. If two concepts are related in the context of the framing applied

to a text, they are also likely to frequently appear within several words of each other (Doerfel &

Barnett, 1999). This approach has theoretic foundations grounded in cognitive processes (Scott,

2005). Words are hierarchically clustered in memory and their meaning is retrieved through

associations with other words. If we assume that some patterns of those cognitive associations

emerge in written text, semantic maps could be one way to capture them (Doerfel, 1998).


This type of analysis has been applied to the study of media texts, extracting important

themes, central ideas, and the connections between them. Semantic studies have been used to

examine journalistic representations of nicotine (Murphy, 2001), artificial sweeteners (Hellsten,

Dawson, & Leydesdorff, 2009), the SARS crisis (Tian & Stewart, 2005), and political actors

(Danowski & Cepela, 2009; Van Atteveldt, Kleinnijenhuis, & Ruigrok, 2008), among others.

More detailed discussion of the interplay between semantic maps and media frames, as well as of

their impact on audience members can be found in the inter-sector section of this article.

Once the semantic map is compiled, it can be interpreted directly based on the

researcher's knowledge of the domain, or used to derive other measures and perform different

types of quantitative analyses (Rice & Danowski, 1993). Clustering analysis, for example, may

be used to examine groups of concepts that tend to appear together. Different frames emerge as

different clusters of the focal concepts. Another type of analysis takes separate bodies of texts

and constructs a semantic network for each one of them. Network correlations can be used to

assess the level of similarity between different semantic maps over the same concepts (Doerfel &

Barnett, 1999).

Though it has its disadvantages (e.g. problems with precision and flexibility), automated

semantic mapping is likely to produce more consistent results across texts than human coders

(Vlieger & Leydesdorff, 2011). It is, furthermore, particularly well-suited for analysis of large

corpora of media content which would present some challenge to manual coding.

The Audience Sector: Social Networks [3-3]

The third type of network-centric research studies media distribution and consumption.

The focus is on audience members and the connections between them. Those connections may

be social ties of friendship, common affiliations and media preferences, as well as links based on

information exchange (See Figure 1, [3-3], [3-2], [3-1]).

In the context of media research, communication connections between audience

members are crucial as they provide an infrastructure allowing the spread of media preferences

and the diffusion of content. This is especially visible online: social consumption of news is both


easy to track and ubiquitous on the Web (Purcell, Rainie, Mitchell, Rosenstiel, & Olmstead,

2010).

The pattern of ties between individuals is a well-studied research area. In the field of

social networks, a host of mechanisms underlying the structure of interpersonal relationships has

been identified. Those include, among others, balance, homophily, and proximity theories

(Monge & Contractor, 2003). Balance models suggest that two people are more likely to be

connected if they have friends in common. In a network context, this manifests in a tendency

towards triadic transitivity. The premise of balance theory is borrowed from cognitive

consistency frameworks (Cartwright & Harary, 1956; Heider, 1946) which propose that friends

will evaluate objects in a similar fashion and, as a corollary, will have similar attitudes towards

the people they meet.

Homophily is colloquially known as the "birds of a feather flock together" principle:

individuals have a preference for social ties with those who are similar to them (Monge &

Contractor, 2003). Associating with like-minded others makes interactions more predictable,

reducing the potential stress or discomfort associated with encountering diversity (Brass, 1995).

As a result, personal networks are often homogenous with regard to multiple socio-demographic

characteristics including age, gender, race, religion, and occupation (McPherson, Smith-Lovin, &

Cook, 2001).

Another closely related theory proposes geographic proximity as an important factor

predicting interpersonal relationships. Occupying the same physical space gives people

opportunities for interaction, thus facilitating closer relationships. Spatial propinquity has been

found to foster the formation and maintenance of ties between similar individuals (Preciado,

Snijders, Burk, Stattin, & Kerr, 2011).

Studying the impact of communication technologies on relationship patterns, scholars

have investigated the principles affecting online tie formation. Internet interactions may be

expected to relax somewhat the constraints of proximity and homophily, as they allow for

diverse and long-distance relations (Cairncross, 1997). Yet geography and similarity remain

crucial for social structures. In one example, a recent study of social ties on Twitter demonstrates

that a substantial portion of ties on the platform were formed between users in the same

metropolitan region (Takhteyev, Gruzd, & Wellman, 2011). Ties connecting individuals across


regional clusters, furthermore, were predicted by distance, nationality and language. Research

further indicates that online social networks tend to mirror close personal relationships from the

offline world (Hampton, Goulet, Rainie, & Purcell, 2011).

Inter-Sector Networks

In this section we review inter-sector networks: structures that involve nodes from a focal

domain, but have additional nodes and relations from one or more of the other two domains. For

instance, the following two sections discuss adding content elements (Industry-Content) or

audience-defined nodes and links (Industry-Audience) to interorganizational networks of media

venues.

Industry-Content Networks: News Organizations and Content Production [1-2]

Organizational ecology suggests that the content produced by media outlets is bound to

be influenced by some of their organizational characteristics. Larger, generalist companies have

broad content niches in that they provide a wide variety of materials in an attempt to appeal to

large audiences. Smaller, specialized news enterprises have narrow niches in that they focus on a

limited number and type of stories (Dimmick, 2003). At an institutional level, selection of stories

depends on established internal routines. Those routines are subject to the variation-selection-

retention mechanisms discussed earlier.

Media outlets vary in type, location, projected identity, social context, target audiences,

political orientation, production technologies, available resources, and ties to other organizations.

All of those characteristics – and many more – affect content production (Allern, 2002;

McManus, 2008). It is only to be expected that the properties of an individual outlet will affect

the type, volume, and diversity of its content. What is more interesting is that content is also

influenced by the links between organizations. The network relationships described in the

previous section - financial, corporate, interpersonal - have considerable bearing on the

production process and output.


Ownership and partnership ties are particularly important in that they influence the

homogenization and diversification of media content. Research has found that the corporate

policies of parent companies affect the news agenda of their subsidiaries - as do ties with

advertisers and sponsors (Duplessis & Li, 2004). The impact is not only due to the adoption of

formal policies. As corporations seek economies of scale, they share organizational knowledge

and resources, including information and staff, between the media outlets they own. As a result,

journalistic and editorial practices are transferred between different news sources. Studies in this

area have confirmed the impact of ownership structure on news quality (Dunaway, 2008) and

diversity (Huber, 2006).

In addition to being affected by links between organizations, media content can also

constitute a link in itself. One potential tie of that kind is created through the exchange of stories

between partnering news outlets. Another possibility, suggested by Ognyanova (2010), involves

defining links based on the overlap of issue coverage between media outlets. Researchers

studying the interdependencies between the topics covered by different news providers often

employ the mass communication paradigm of intermedia agenda-setting. Theory suggests that

elite media like the New York Times have the ability to influence the topic selection of other

outlets (Rogers, Dearing, & Bregman, 1993). The current dominant approach in agenda setting

research involves computing correlations between rank-ordered lists of media issue priorities

(Coleman & McCombs, 2007). While useful in demonstrating similarities, this method is

problematic when trying to answer more specific questions about directions of influence,

centrality of outlets and external factors affecting the agenda overlap. Moreover, it does not

provide a particularly helpful description of the global patterns of shared issue priorities. A

network approach, on the other hand, allows for a more sophisticated exploration of the social

influence between media organizations and content selection.

Industry-Audience Networks: Media Companies and Online Audiences [1-3]

As media outlets increasingly offer their content on the Web, researchers have developed

online methods to trace patterns of influence. News websites, like nytimes.com, can be seen as

online representations of their respective media organizations - in this case, The New York


Times. Hyperlinks embedded in the content constitute the network connections. While the

analysis of online link structures may present methodological challenges (Barnett, Chung, &

Park, 2011), this approach provides a rare opportunity for longitudinal studies of online patterns,

tracking Web configurations over time (Park, Barnett, & Chung, 2011).

A number of scholars have used the hyperlink perspective to explore the ties that exist

between mainstream news sites and blogs (Kelly, 2008; Meraz, 2009; Tremayne, 2006; Turow &

Tsui, 2008). For the purposes of those studies, blogs are often implicitly regarded as a special,

more opinionated, individualized online form that gives voice to audience members. Online

presence of this kind allows news consumers to interact with professional journalists, potentially

engaging them in a productive conversation. In some prominent cases, bloggers have succeeded

in influencing the US media industry agenda and focusing public attention on previously ignored

issues (Ward, Cahill, & Petelin, 2007).

Three major approaches to deriving meaning from hyperlinks can be identified in existing

research. The distinctions stem from viewing online links as signifying affiliation, similarity, or

value. The first two approaches posit that more linking is likely to occur between the websites of

companies or people who share some common ground. The affiliation frame regards hyperlinks

as a proxy for social and organizational relationships (Mika, 2007; Park & Thelwall, 2003). This

may refer to existing relations such as those between people who know each other, between

organizations that work together or between subsidiaries of the same parent company (Ali-Hasan

& Adamic, 2007; Weber, 2012; Weber & Monge, 2011).The similarity frame views the

existence of online links as an indicator of a different type of commonality based on shared

properties. Elite news venues may, for instance, only post links to other outlets of the same type

(Meraz, 2009). Political bloggers may overwhelmingly link to people with similar political

ideology (Adamic & Glance, 2005; Nahon & Hemsley, in press; Park & Thelwall, 2008).

Network methods, which take into account individual attributes, dyadic connections, and

the dependencies between the two, are particularly well-suited to study these processes. Network

clustering provides one way to explore the tendency of similar online actors to link to each other.

Another method that has been used to examine the social patterns of linking in blogs employs

community detection algorithms (Chin & Chignell, 2006).


The value frame, the third view on the significance of online connections, sees the

hyperlink as an information exchange tie. Links are expected to point to content or organizations

that are considered relevant, credible, and authoritative (Park, Barnett, & Nam, 2002). Similar to

citations in scientometric research, hyperlinks are treated as indicators of quality and as markers

for reputation (Park & Thelwall, 2003; Thelwall, 2009). In its simplest version, this approach can

assess the influence of a media website based on its in-degree centrality, calculated in this case

as the number of hyperlinks pointing to the site. More sophisticated network algorithms have

also been developed to explore the relevance of online content within a specific knowledge

domain (Easley & Kleinberg, 2010). One framework suggests distinguishing between sites

containing valuable new material, called sources, aggregation sites that filter information and

add value, called authorities, and sites which “collect links and direct users to the most relevant

or appropriate information for a given topic,” called hubs (Kleinberg, Kumar, Raghavan,

Rajagopalan, & Tomkins, 1999; Weber & Monge, 2011).

Researchers looking into patterns of online influence have found that mechanisms of

preferential attachment are shaping the structure of the Web. "The rich get richer" mechanism

leads to a power law distribution2 of links to mainstream media and blog sites (Drezner &

Farrell, 2004). Studies have associated those findings with the existence of an elite bias. There is

evidence that a small number of high-profile news venues have a major impact over the rest of

the media and the public opinion (Pew Project For Excellence in Journalism, 2010, 2011).

Link analysis has thus emerged as a useful new method in the area of agenda-setting

research. The ability of prominent news sources to set the issue coverage priorities of other

outlets and audience members is assessed through patterns of hyperlinking. In one study of that

kind, Meraz (2009) looks at links to examine the power of the New York Times and the

Washington Post to set the political news agenda. Her results suggest that mainstream media

outlets, while not the sole source of influence online, remain dominant agenda-setters on the

web.

While most of the articles referenced in this section do not employ multiplex designs in

their study of ties between news sources and individuals, this approach can provide a useful

2 That is to say, the probability that a web page will have k incoming links is inversely proportional to a power of k. In particular, k is roughly proportional to 1/k2 (Easley & Kleinberg, 2010)


analytical framework for future investigations in the area. In addition to looking into

organizational ties, for example, scholars may explore a second type of connection between

online media that is constructed through web co-link analysis (Zuccala, 2006). This technique

allows researchers to evaluate the relationship between two news sites based on the number of

cases in which individual audience members have linked to both.

In the context of a media interorganizational network, co-link relationships provide a new

set of ties determined by co-occurrence of hyperlinks generated by the general public. Using

analytical tools similar to those of bibliometrics, researchers can further identify clusters of news

sources that typically get "cited" together by consumers (Thelwall & Wilkinson, 2004).

Another key application of hyperlink analysis emerges from world system theory studies

investigating international flows of information (Park et al., 2011). Works in that line of research

explore the structure of online and telecommunication networks among countries, identifying a

core-periphery structure with central positions reserved for first-world rich economies (Barnett,

Jacobson, Choi, & Sun-Miller, 1996). One relevant aspect of world systems theory lies in its

capacity to predict the prominence of nations in foreign affairs news (Chang, 1998). While

international networks are beyond of the scope of this work, as nations do not fall into the core

node types examined by mass communication research (e.g. audience members, media

organizations, and content),this application of link analysis deserves to be mentioned as a fruitful

direction for further research.

Content-Audience Networks: Framing Research and Public Opinion [2-1]

One important aspect of the interaction between content networks and audience members

is captured by the classic theory of framing. The main idea behind it is that media can affect the

way we think about an issue by making some of its aspects more salient while ignoring others.

Framing is not necessarily limited to a media "spin" on controversial issues. Rather it serves as a

parsing mechanism, a collective sense-making tool aiding the understanding of everyday events

and social interactions (Goffman, 1974). Recognizing the significance of frames as socially

shared, persistent organizing principles that structure meaning (Reese, 2007), scholars in media

and political studies began looking for ways to access those structures. Developing a practical


definition of framing that can be used in empirical research is recognized as a notoriously

difficult task (Koenig, 2004). One attempt to do that comes from Entman (1993), who suggested

looking for the presence or absence of certain "keywords, stock phrases, stereotyped images,

sources of information and sentences that provide thematically reinforcing clusters of facts or

judgments". Semantic network analysis provides a useful set of methods allowing scholars to

identify central concepts, evaluate the relationships between them, and uncover the constellations

of terms or ideas that tend to cluster together.

Most of the research on framing confirms the assumption that media frames have a

powerful impact on public opinion (Castells, 2009; Shen & Edwards, 2005). Pan and Kosicki

(2001) go as far as to suggest that it is an essential part of public deliberation. Semantic links

established in the news should then have an observable impact on its consumers.

Guo and McCombs (2011) further developed this line of research, testing empirically the

capability of semantic relationships present in media content to influence the cognitive concept

maps held by audience members. Their study found similarities between media-generated and

public-opinion semantic networks of attributes about Texas gubernatorial candidates. Analysis of

this type provides a good example of multiplex thinking about the media system. Guo and

McCombs in effect study a media content network with an additional set of semantic links

generated by audience members.

Content-Industry Networks: Framing Research and Media Organizations [2-3]

Researchers have applied semantic analysis in a wide diversity of contexts to explore the

implications of media framing. SNA research has examined political debates, organizational

literature, media framing of genetic testing, health crises, nicotine, artificial sweeteners and other

topics (Hellsten et al., 2009; Murphy, 2001; Murphy & Maynard, 2000; Samkin & Schneider,

2008; Tian & Stewart, 2005). These studies analyze clusters of terms frequently used within a

thematic domain in order to identify implicit frames employed by media outlets or political

factions.

Semantic mapping can be particularly helpful in studies that bridge the content and

organizational levels of analysis. Research may, for instance, explore the theoretically predicted


similarity between the frames employed by news outlets which are part of the same media

conglomerate (Allern, 2002; McManus, 2008). Scholars have already used SNA to compare the

framing strategies of different media companies (L. Kim, 2011; Tian & Stewart, 2005). Work in

this area of content research can be further advanced through a multiplex network perspective.

One direction for future research in this field involves analyzing news stories produced by

different media organizations and testing whether interorganizational links are associated with

semantically similar structures of content.

In addition to providing an instrument for the analysis of news content, a semantic

approach can be applied to study the internal discourse of media companies. As Monge & Poole

(2008) suggest, the texts and conversations generated by an organizational discourse can be

viewed as networks of intertextual and reflexive links. Constructing those links as semantic

relationships allows for the study of discourse structure and evolution over time, giving a

network interpretation to the symbolic and rhetorical dynamics involved in the process.

SNA is expected to become more methodically sophisticated and more popular as a

research technique with the advent of the Semantic Web (O’Hara, Berners-Lee, Hall, &

Shadbolt, 2010). The key driving force behind that new strategy of information representation is

the use of formal languages that computers can process to describe the meaning of online content

(Mika, 2007). Embedded metadata will allow machines to understand the context of information,

combining facts from multiple sources to perform an ever more intelligent knowledge analysis.

Media outlets like the New York Times have already begun releasing their stories under a new

linked open data format (The New York Times, 2009). This makes it possible for computers to

automatically determine which names in the text refer to people, organizations or places, and

retrieve background information about those entities.

Audience-Industry Networks: Explaining Media Preferences [3-1]

In the realm of media studies, multiple paradigms provide insight into audience

relationships with news organizations. Media system dependency suggests that individuals rely

on media for their information needs such as understanding the environment, learning social

norms, and escaping from everyday pressures through entertainment (Ball-Rokeach, 1985; Ball-


Rokeach & DeFleur, 1976). The uses and gratifications approach (Katz, Blumler, & Gurevitch,

1973) similarly emphasizes the role of the consumer in selecting the appropriate media to meet

their needs.

One perspective which explicitly focuses on both social ties and news organizations is

communication infrastructure theory or CIT (Ball-Rokeach, Kim, & Matei, 2001; Y. C. Kim &

Ball-Rokeach, 2006). The framework builds on media system dependency (Ball-Rokeach, 1998),

investigating interpersonal and mediated interactions along with their civic outcomes (Ball-

Rokeach et al., 2001). It proposes a holistic, multi-method approach to studying the community

storytelling network, an integrated system encompassing local residents, organizations and media

outlets. CIT emphasizes the importance of both interpersonal ties and engagement with media as

factors contributing to civic engagement. The social networks connecting residents, as well as

the affiliation links between residents and their preferred media outlets, are among the important

aspects of the local storytelling system. Higher-level multiplex structures are implied here as we

can envision an additional set of ties between individuals constructed on the basis of their shared

news source preferences.

In the ideal case, CIT suggests that micro-level agents (residents) and meso-level agents

(organizations and news media) should form a single, well-integrated network (Matei & Ball-

Rokeach, 2002). As the Internet becomes an increasingly important part of local communication

infrastructures, ties between audience members and online news organizations become another

relevant set of connections linking micro- and meso-level actors (Matei & Ball-Rokeach, 2001).

Research in this area has already identified a number of individual properties affecting

the patterns of ties between audience members and local news providers. Generational

differences, socio-economic status and ethnicity may have an impact on both interpersonal

contacts and individual links to media organizations (Y. C. Kim & Ball-Rokeach, 2006).

Audience-Content Networks: Diffusion of News Stories [3-2]

In the context of media studies, social structures can be seen as a part of a larger system.

Online and offline connections between audience members form the infrastructure over which

media content can be propagated.


A classic media effects theory, the two-step flow of communication, provides the basis

for network studies of media content diffusion. The model suggests that instead of reaching the

public directly, the ideas broadcasted by media outlets are channeled through a particularly

active segment of audience members: the opinion leaders (Katz, 1957; Katz & Lazarsfeld, 1955).

Translating the two-step flow into network terms, we can see the mechanism it describes as a

diffusion process (Valente, 1996). Certain central, well-connected nodes in the network, known

as the opinion leaders, pick up ideas or other information from the media. That information is

then disseminated further through their interpersonal network connections.

The diffusion of media content has been particularly well-studied in an online context. In

the past few years, Web platforms have started providing multiple tools that allow users to

reconstruct their real life social networks in an online space (Kleinberg, 2008). Sites like

Facebook and Twitter give audience members the option to forward media content to their

connections with the click of a button. A recent Pew report suggests that that's exactly what 37%

of U.S. Internet users are doing (Purcell et al., 2010). More than half of the people using social

networking sites, furthermore, receive and follow links to news items on a daily basis.

Blogs are another medium in which news content is propagated. As it is an accepted

norm for bloggers to link to their sources (Chin & Chignell, 2006; Ferdig & Trammell, 2004),

information diffusion in the blogosphere can be tracked based on hyperlink patterns and time

stamps.

Taking into account these new trends, scholars have started studying the spread of topics

through both social networking platforms (Oh, Susarla, & Tan, 2008) and blogs (Leskovec,

Backstrom, & Kleinberg, 2009; Leskovec, McGlohon, Faloutsos, Glance, & Hurst, 2007). The

two analytical approaches used to explore the online diffusion of media content involve

threshold models (Valente, 1996) and cascade models (Cointet & Roth, 2009). In threshold

models, an actor's decision to disseminate a topic is based on the proportion of their connections

who have already started discussing the subject. In a cascade model, each time an actor is

"infected" with a new topic, there is a certain probability that the infection will spread to

neighboring nodes.

Epidemic models of diffusion are often used for online content as their robustness has

been well established through long use in other scientific fields. Much of the research looking


into the network flow of information is based on methods initially developed to model the spread

of disease through interpersonal connections. Clinical research in epidemiology often uses the

SIR (susceptible - infected - recovered) cycle to describe the stages through which a node may

pass (Easley & Kleinberg, 2010; Lewis, 2009). The same model has been adapted to study

audience members and their exposure to media content (Leskovec et al., 2007). In the online-

diffusion version of SIR, users may become susceptible to a topic when it is suggested to them

by a friend (either through a blog post or via a service like Twitter or Facebook). The person may

then be infected with the topic, in that they write a post about it or publish it on a social

networking platform. With this, the individual is considered to have recovered from the topic,

although a relapse is possible when something new appears on the subject.

Modeling the spread of media content through social networks has allowed researchers to

understand topic life-cycles, spikes and declines (Cointet, Faure, & Roth, 2007; Gruhl, Guha,

Liben-Nowell, & Tomkins, 2004; Leskovec et al., 2007). It has also provided a way to explore

patterns of influence and identify opinion leaders (Java, 2006; Nakajima, Tatemura, Hara,

Tanaka, & Uemura, 2006).

Conclusion, Limitations and Future Research

This article has presented a multilevel, multitheoretical, and multidimensional network

model of the media system. Three major sectors were described: the media industry, content, and

audiences. The role of networks linking nodes within these three sectors, called intra-sector

networks, was examined. Inter-sector networks were further introduced as a way to tie together

entities from different domains, creating macro-level networks among different components of

the media system. Intra- and inter-sector network interactions, including multiplex and

multidimensional structures, were shown to influence media production, output, and

consumption. Existing studies and theories that bridge two or all three media domains were

briefly discussed. Grounded in Monge & Contractor's (2003) multilevel, multitheoretical

network model, expanded to include multidimensional links, we suggested that complementary

theoretical mechanisms and analytical tools are needed to explain network phenomena in the

increasingly interconnected media system (Castells, 2009).


Several of those combinations deserve to be highlighted here as they provide particularly

promising directions for future research:

- Ecological theory and organizational networks. This group of theoretical tools allows for the

construction of networks between media outlets based on resource exchange, competition,

ownership, partnership, or strategic alliances. A study taking this approach may explore the

effects of strategic content and audience resources (Dimmick, 2003) on organizational

networks.

- Agenda-setting theory and hyperlink networks. As media organizations and audience members

coexist on the Web and link to each other's content, patterns of influence can be detected from

existing hyperlink networks. Studies in that area can identify prominent news outlets, but they

can also look for bottom-up effects of topics crossing from audience members to mainstream

media.

- Framing theory and semantic networks. This type of analysis is set to become widely used in

the social sciences as the Semantic Web and open linked data format allow an intelligent

automated parsing of media content. Semantic maps are already used to identify the dominant

frames of media texts. While this approach focuses on content, it has potential applications

bridging the levels of media organizations and consumers. Semantic tools may, for example,

be used to compare the framing of issues between different news outlets.

- Communication infrastructure theory (CIT) and social networks. CIT is explicitly formulated

in network terms, as it emphasizes the importance of links between residents, media and

community organizations. The storytelling system described by the theory incorporates social

relations based on local information exchange. Research using this framework may

investigate the structure, density and clustering patterns of the storytelling network. Central

actors, as well as actors serving as bridges between different groups can be identified.

- Diffusion theories and semantic or hyperlink networks. Social contagion and diffusion

theories are used to track the online spread of topics or specific stories between media outlets

and consumers. Extracting the underlying topics from text requires the use of semantic

parsing (Leskovec et al., 2009). Tracking the spread of specific media stories can also be done

based on hyperlink structure. Studies in the area model the diffusion, trying to identify

cyclical patterns, influential nodes, and mechanisms guiding the dissemination.


While this work focuses on the promise of relational approaches to media studies, we

recognize a number of limitations that also need to be taken into consideration. Network data is,

by its nature, relatively difficult to collect (Hanneman & Riddle, 2005). Studying full networks

may be an expensive and lengthy exercise. Moreover, because of the relational focus, sampling

strategies may be difficult to design. Network researchers frequently encounter boundary

specification problems (Marin & Wellman, 2010). While it is usually easy to identify all

members of a particular group, the same is not true of networks (Marsden, 2005). Deciding

where a network "ends" can be challenging, as networks often have no natural boundaries

(Borgatti & Halgin, 2011).

As with other statistical techniques, establishing causality is a major challenge in network

research (Fowler, Heaney, Nickerson, Padgett, & Sinclair, 2011). This is particularly problematic

in studies which need to determine whether social ties are the cause or consequence of individual

attributes (Shalizi & Thomas, 2011).

While the challenges in data collection and analysis are serious, it is our belief that most

of them are far from permanent. The parallel advancement of network theories and methods is

living up to its early promise in the social as well as the physical sciences (Borgatti et al., 2009).

In a new media landscape characterized by networked production, networked distribution and

networked consumption, relational thinking should be an essential aspect of theorizing and

research.


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Table 1

Theories Explaining the Formation and Structural Properties of Networks

within and between the Domains of Media Industry, Content and Audiences

Domains

under Study 1. Media Industry 2. Media Content 3. Media Audiences

1. Media Industry Evolutionary Theories, Population ecology, Theory of the Niche Resource Dependence Exchange Theories Equilibrium Theories

2. Media Content Evolutionary Theories, Population ecology, Theory of the Niche Intra and inter-media

level agenda-setting Media bias

Semantic Networks, cognitive concept mapping, Framing theories

3. Media Audiences Media Ecology Selective exposure Uses & Gratifications Media System

Dependency Information Seeking Transaction cost

economics

Two Step Flow Theory Contagion/Diffusion Social Capital Structural Holes Collective Action,

Public Goods

Homophily theories Proximity theories Electronic

Propinquity Balance theories CIT & Storytelling

Networks


Figure 1. The Composition of Media Networks

Note: The numbers within brackets represent the three media sectors: (1) industry, (2) content,

and (3) audiences. The sequence of numbers in square brackets indicates that a network is

composed by nodes in the sector represented by the first number and relations/nodes from the

sector represented by the second number. Intra-sector networks are represented by the same

numbers: 1-1, 2-2, and 3-3. Inter-sector networks are represented by different numbers, e.g., 1-2,

3-1.

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A Multitheoretical, Multilevel, Multidimensional … Network Model...Running head: A NETWORK MODEL OF THE MEDIA SYSTEM A Multitheoretical, Multilevel, Multidimensional Network Model